A Real World Visual SLAM Dataset for Indoor Construction Sites
This paper presents a RGBD slam construction dataset with a mounted platform, designed to collect the unique challenges encountered in construction sites. An Ouster OS0-128 LiDAR is utilized as the sensor of LiDAR SLAM, working as the ground truth for localization. Our dataset records various construction settings with different stages of building materials and structures, such as concrete, brick, plaster, and putty, providing a comprehensive benchmark for training and evaluating SLAM algorithms. Through testing on current SLAM algorithms, we demonstrate the limitations of traditional approaches in these environments and provide a VINS based algorithm as the benchmark. This dataset serves as a valuable resource for researchers aiming to enhance SLAM performance in the real construction environments. The detailed information of the dataset is available at https://github.com/WenyuLWY/HCIC-Construction-VSLAM-Dataset.git
Authors
- Yantao Yu (ORCID: https://orcid.org/0000-0003-0400-3068)
- Wenyu Li
- Xinyu Chen (ORCID: https://orcid.org/0000-0001-7655-7823)
Publication Details
- Journal
- Kalpa publications in computing
- Published
- 2025-08-28
- DOI
- https://doi.org/10.29007/csqt
- Primary Topic
- 3D Surveying and Cultural Heritage
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Hong Kong University of Science and Technology